{"id":10391,"date":"2018-06-20T00:00:00","date_gmt":"2018-06-20T05:00:00","guid":{"rendered":"https:\/\/threecloud.wpengine.com\/post\/overview-of-azure-machine-learning\/"},"modified":"2023-09-18T15:19:20","modified_gmt":"2023-09-18T22:19:20","slug":"overview-of-azure-machine-learning","status":"publish","type":"post","link":"https:\/\/3cloudsolutions.com\/resources\/overview-of-azure-machine-learning\/","title":{"rendered":"Overview of Azure Machine Learning"},"content":{"rendered":"

Are you looking to do some machine learning or predictive analysis in your business? In this post, I\u2019ll give an overview of Azure Machine Learning. Azure Machine Learning<\/a> is a service and a toolset used for building predictive models. This cloud-based design interface allows you to design each step in the machine learning process.<\/p>\n

Let\u2019s look at those steps:<\/p>\n

1. First, you need some data.<\/strong> The machine learning tools allow you to import data and tools are built in for analysis of that data, so you can do some quick visualizations and profiling. Modules are also built in for data cleansing, such as removing missing values, removing or dealing with outliers, doing data normalization to bring things into range for certain models. If you\u2019ve written your own R or Python scripts that do this, you can integrate those scripts into your machine learning model.<\/p>\n

2. Next, the machine learning toolset allows you to choose a model.<\/strong> In broad terms, choosing from either a progression model, a classification model, a clustering model or a model for anomaly detection.<\/p>\n

3. Now it\u2019s time to test the model.<\/strong> There\u2019s tooling available that allow you to split your dataset into 2 pieces. One that you\u2019ll use to train your model and then after your model is trained, you use the other half to do testing. All these tools are available in the Azure Machine Learning toolset.<\/p>\n

The process is different than other technical problems in that there\u2019s no definitive goal that you\u2019re searching for. By nature, it\u2019s an iterative process; the machine learning tools make it easy for you to iterate, try different models and tune them and test them side by side to evaluate.<\/p>\n

4. Once you\u2019ve selected a model, it\u2019s time to deploy it.<\/strong> With the Azure Machine Learning tools, you can deploy to a web service, then the web service can be integrated into an application. For example, if you had an application and you were accepting some user input, you could take that input and feed it into a machine learning model that would render a prediction on the fly.<\/p>\n

With the web service, you could also use Azure Data Factory for batch style predicting by inputting a batch of data and have that output a batch of predictions that you could store in a data warehouse or some other reporting application.<\/p>\n